res: Results from 'distinct_test' function

In distinct: distinct: a method for differential analyses via hierarchical permutation tests

Description

Results from distinct_test function

Arguments

res	contains a data.frame object, with the results obtained applying distinct_test function to Kang_subset dataset. Below the code used to obtain 'res'.

Author(s)

Simone Tiberi [e-mail]

See Also

distinct_test

Examples

# load the input data:
# data("Kang_subset", package = "distinct")
# Kang_subset
# 
# create the design of the study:
# samples = Kang_subset@metadata$experiment_info$sample_id
# group = Kang_subset@metadata$experiment_info$stim
# design = model.matrix(~group)
# rownames of the design must indicate sample ids:
# rownames(design) = samples
# design
# 
# Note that the sample names in `colData(x)$name_sample` have to be the same ones as those in `rownames(design)`.
# rownames(design)
# unique(SingleCellExperiment::colData(Kang_subset)$sample_id)
# 
# In order to obtain a finer ranking for the most significant genes, if computational resources are available, we encourage users to increase P_4 (i.e., the number of permutations when a raw p-value is < 0.001) and set P_4 = 20,000 (by default P_4 = 10,000).
# 
# The group we would like to test for is in the second column of the design, therefore we will specify: column_to_test = 2
# 
# set.seed(61217)
# res = distinct_test(
#   x = Kang_subset, 
#   name_assays_expression = "logcounts",
#   name_cluster = "cell",
#   design = design,
#   column_to_test = 2,
#   min_non_zero_cells = 20,
#   n_cores = 2)
#   
#   save(res, file = "res.RData")
#   saveRDS(res, file = "res.rds")

distinct documentation built on Nov. 8, 2020, 8:20 p.m.